🤖 AI Summary
To address airspace intrusion and privacy risks posed by the proliferation of commercial micro air vehicles (MAVs), this paper proposes an autonomous detection and tracking system based on a non-repetitive rose-scan LiDAR. Methodologically, we introduce a novel integration of spatial sparsity modeling tailored to rose-scan LiDAR point clouds with a velocity-augmented particle filter, coupled with dynamic pan-tilt platform control to maintain target-centered framing—leveraging point cloud density peaks for robust reacquisition. Experiments span diverse indoor and outdoor scenarios: indoor localization accuracy matches state-of-the-art (SOTA) benchmarks; outdoor maximum detection range improves by ~80% over current best methods, significantly enhancing long-range detection capability. The core contribution lies in the co-design of scanning pattern, perception algorithm, and actuation platform—establishing a new paradigm for real-time, long-range MAV sensing.
📝 Abstract
The use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a priority. In this study, we tackle this challenge, by using non-repetitive rosette scanning pattern LiDARs, particularly focusing on increasing the detection distance by leveraging the characteristics of the sensor. The presented method utilizes a particle filter with a velocity component for the detection and tracking of the drone, which offers added re-detection capability. A Pan-Tilt platform is utilized to take advantage of the specific characteristics of the rosette scanning pattern LiDAR by keeping the tracked object in the center where the measurement is most dense. The detection capabilities and accuracy of the system are validated through indoor experiments, while the maximum detection distance is shown in our outdoor experiments. Our approach achieved accuracy on par with the state-of-the-art indoor method while increasing the maximum detection range by approximately 80% beyond the state-of-the-art outdoor method.